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Deep Learning-Based Classification of Cancer Cell in Leptomeningeal Metastasis on Cytomorphologic Features of Cerebrospinal Fluid

BACKGROUND: It is a critical challenge to diagnose leptomeningeal metastasis (LM), given its technical difficulty and the lack of typical symptoms. The existing gold standard of diagnosing LM is to use positive cerebrospinal fluid (CSF) cytology, which consumes significantly more time to classify ce...

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Autores principales: Yu, Wenjin, Liu, Yangyang, Zhao, Yunsong, Huang, Haofan, Liu, Jiahao, Yao, Xiaofeng, Li, Jingwen, Xie, Zhen, Jiang, Luyue, Wu, Heping, Cao, Xinhao, Zhou, Jiaming, Guo, Yuting, Li, Gaoyang, Ren, Matthew Xinhu, Quan, Yi, Mu, Tingmin, Izquierdo, Guillermo Ayuso, Zhang, Guoxun, Zhao, Runze, Zhao, Di, Yan, Jiangyun, Zhang, Haijun, Lv, Junchao, Yao, Qian, Duan, Yan, Zhou, Huimin, Liu, Tingting, He, Ying, Bian, Ting, Dai, Wen, Huai, Jiahui, Wang, Xiyuan, He, Qian, Gao, Yi, Ren, Wei, Niu, Gang, Zhao, Gang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8904144/
https://www.ncbi.nlm.nih.gov/pubmed/35273914
http://dx.doi.org/10.3389/fonc.2022.821594
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author Yu, Wenjin
Liu, Yangyang
Zhao, Yunsong
Huang, Haofan
Liu, Jiahao
Yao, Xiaofeng
Li, Jingwen
Xie, Zhen
Jiang, Luyue
Wu, Heping
Cao, Xinhao
Zhou, Jiaming
Guo, Yuting
Li, Gaoyang
Ren, Matthew Xinhu
Quan, Yi
Mu, Tingmin
Izquierdo, Guillermo Ayuso
Zhang, Guoxun
Zhao, Runze
Zhao, Di
Yan, Jiangyun
Zhang, Haijun
Lv, Junchao
Yao, Qian
Duan, Yan
Zhou, Huimin
Liu, Tingting
He, Ying
Bian, Ting
Dai, Wen
Huai, Jiahui
Wang, Xiyuan
He, Qian
Gao, Yi
Ren, Wei
Niu, Gang
Zhao, Gang
author_facet Yu, Wenjin
Liu, Yangyang
Zhao, Yunsong
Huang, Haofan
Liu, Jiahao
Yao, Xiaofeng
Li, Jingwen
Xie, Zhen
Jiang, Luyue
Wu, Heping
Cao, Xinhao
Zhou, Jiaming
Guo, Yuting
Li, Gaoyang
Ren, Matthew Xinhu
Quan, Yi
Mu, Tingmin
Izquierdo, Guillermo Ayuso
Zhang, Guoxun
Zhao, Runze
Zhao, Di
Yan, Jiangyun
Zhang, Haijun
Lv, Junchao
Yao, Qian
Duan, Yan
Zhou, Huimin
Liu, Tingting
He, Ying
Bian, Ting
Dai, Wen
Huai, Jiahui
Wang, Xiyuan
He, Qian
Gao, Yi
Ren, Wei
Niu, Gang
Zhao, Gang
author_sort Yu, Wenjin
collection PubMed
description BACKGROUND: It is a critical challenge to diagnose leptomeningeal metastasis (LM), given its technical difficulty and the lack of typical symptoms. The existing gold standard of diagnosing LM is to use positive cerebrospinal fluid (CSF) cytology, which consumes significantly more time to classify cells under a microscope. OBJECTIVE: This study aims to establish a deep learning model to classify cancer cells in CSF, thus facilitating doctors to achieve an accurate and fast diagnosis of LM in an early stage. METHOD: The cerebrospinal fluid laboratory of Xijing Hospital provides 53,255 cells from 90 LM patients in the research. We used two deep convolutional neural networks (CNN) models to classify cells in the CSF. A five-way cell classification model (CNN1) consists of lymphocytes, monocytes, neutrophils, erythrocytes, and cancer cells. A four-way cancer cell classification model (CNN2) consists of lung cancer cells, gastric cancer cells, breast cancer cells, and pancreatic cancer cells. Here, the CNN models were constructed by Resnet-inception-V2. We evaluated the performance of the proposed models on two external datasets and compared them with the results from 42 doctors of various levels of experience in the human-machine tests. Furthermore, we develop a computer-aided diagnosis (CAD) software to generate cytology diagnosis reports in the research rapidly. RESULTS: With respect to the validation set, the mean average precision (mAP) of CNN1 is over 95% and that of CNN2 is close to 80%. Hence, the proposed deep learning model effectively classifies cells in CSF to facilitate the screening of cancer cells. In the human-machine tests, the accuracy of CNN1 is similar to the results from experts, with higher accuracy than doctors in other levels. Moreover, the overall accuracy of CNN2 is 10% higher than that of experts, with a time consumption of only one-third of that consumed by an expert. Using the CAD software saves 90% working time of cytologists. CONCLUSION: A deep learning method has been developed to assist the LM diagnosis with high accuracy and low time consumption effectively. Thanks to labeled data and step-by-step training, our proposed method can successfully classify cancer cells in the CSF to assist LM diagnosis early. In addition, this unique research can predict cancer’s primary source of LM, which relies on cytomorphologic features without immunohistochemistry. Our results show that deep learning can be widely used in medical images to classify cerebrospinal fluid cells. For complex cancer classification tasks, the accuracy of the proposed method is significantly higher than that of specialist doctors, and its performance is better than that of junior doctors and interns. The application of CNNs and CAD software may ultimately aid in expediting the diagnosis and overcoming the shortage of experienced cytologists, thereby facilitating earlier treatment and improving the prognosis of LM.
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spelling pubmed-89041442022-03-09 Deep Learning-Based Classification of Cancer Cell in Leptomeningeal Metastasis on Cytomorphologic Features of Cerebrospinal Fluid Yu, Wenjin Liu, Yangyang Zhao, Yunsong Huang, Haofan Liu, Jiahao Yao, Xiaofeng Li, Jingwen Xie, Zhen Jiang, Luyue Wu, Heping Cao, Xinhao Zhou, Jiaming Guo, Yuting Li, Gaoyang Ren, Matthew Xinhu Quan, Yi Mu, Tingmin Izquierdo, Guillermo Ayuso Zhang, Guoxun Zhao, Runze Zhao, Di Yan, Jiangyun Zhang, Haijun Lv, Junchao Yao, Qian Duan, Yan Zhou, Huimin Liu, Tingting He, Ying Bian, Ting Dai, Wen Huai, Jiahui Wang, Xiyuan He, Qian Gao, Yi Ren, Wei Niu, Gang Zhao, Gang Front Oncol Oncology BACKGROUND: It is a critical challenge to diagnose leptomeningeal metastasis (LM), given its technical difficulty and the lack of typical symptoms. The existing gold standard of diagnosing LM is to use positive cerebrospinal fluid (CSF) cytology, which consumes significantly more time to classify cells under a microscope. OBJECTIVE: This study aims to establish a deep learning model to classify cancer cells in CSF, thus facilitating doctors to achieve an accurate and fast diagnosis of LM in an early stage. METHOD: The cerebrospinal fluid laboratory of Xijing Hospital provides 53,255 cells from 90 LM patients in the research. We used two deep convolutional neural networks (CNN) models to classify cells in the CSF. A five-way cell classification model (CNN1) consists of lymphocytes, monocytes, neutrophils, erythrocytes, and cancer cells. A four-way cancer cell classification model (CNN2) consists of lung cancer cells, gastric cancer cells, breast cancer cells, and pancreatic cancer cells. Here, the CNN models were constructed by Resnet-inception-V2. We evaluated the performance of the proposed models on two external datasets and compared them with the results from 42 doctors of various levels of experience in the human-machine tests. Furthermore, we develop a computer-aided diagnosis (CAD) software to generate cytology diagnosis reports in the research rapidly. RESULTS: With respect to the validation set, the mean average precision (mAP) of CNN1 is over 95% and that of CNN2 is close to 80%. Hence, the proposed deep learning model effectively classifies cells in CSF to facilitate the screening of cancer cells. In the human-machine tests, the accuracy of CNN1 is similar to the results from experts, with higher accuracy than doctors in other levels. Moreover, the overall accuracy of CNN2 is 10% higher than that of experts, with a time consumption of only one-third of that consumed by an expert. Using the CAD software saves 90% working time of cytologists. CONCLUSION: A deep learning method has been developed to assist the LM diagnosis with high accuracy and low time consumption effectively. Thanks to labeled data and step-by-step training, our proposed method can successfully classify cancer cells in the CSF to assist LM diagnosis early. In addition, this unique research can predict cancer’s primary source of LM, which relies on cytomorphologic features without immunohistochemistry. Our results show that deep learning can be widely used in medical images to classify cerebrospinal fluid cells. For complex cancer classification tasks, the accuracy of the proposed method is significantly higher than that of specialist doctors, and its performance is better than that of junior doctors and interns. The application of CNNs and CAD software may ultimately aid in expediting the diagnosis and overcoming the shortage of experienced cytologists, thereby facilitating earlier treatment and improving the prognosis of LM. Frontiers Media S.A. 2022-02-22 /pmc/articles/PMC8904144/ /pubmed/35273914 http://dx.doi.org/10.3389/fonc.2022.821594 Text en Copyright © 2022 Yu, Liu, Zhao, Huang, Liu, Yao, Li, Xie, Jiang, Wu, Cao, Zhou, Guo, Li, Ren, Quan, Mu, Izquierdo, Zhang, Zhao, Zhao, Yan, Zhang, Lv, Yao, Duan, Zhou, Liu, He, Bian, Dai, Huai, Wang, He, Gao, Ren, Niu and Zhao https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Oncology
Yu, Wenjin
Liu, Yangyang
Zhao, Yunsong
Huang, Haofan
Liu, Jiahao
Yao, Xiaofeng
Li, Jingwen
Xie, Zhen
Jiang, Luyue
Wu, Heping
Cao, Xinhao
Zhou, Jiaming
Guo, Yuting
Li, Gaoyang
Ren, Matthew Xinhu
Quan, Yi
Mu, Tingmin
Izquierdo, Guillermo Ayuso
Zhang, Guoxun
Zhao, Runze
Zhao, Di
Yan, Jiangyun
Zhang, Haijun
Lv, Junchao
Yao, Qian
Duan, Yan
Zhou, Huimin
Liu, Tingting
He, Ying
Bian, Ting
Dai, Wen
Huai, Jiahui
Wang, Xiyuan
He, Qian
Gao, Yi
Ren, Wei
Niu, Gang
Zhao, Gang
Deep Learning-Based Classification of Cancer Cell in Leptomeningeal Metastasis on Cytomorphologic Features of Cerebrospinal Fluid
title Deep Learning-Based Classification of Cancer Cell in Leptomeningeal Metastasis on Cytomorphologic Features of Cerebrospinal Fluid
title_full Deep Learning-Based Classification of Cancer Cell in Leptomeningeal Metastasis on Cytomorphologic Features of Cerebrospinal Fluid
title_fullStr Deep Learning-Based Classification of Cancer Cell in Leptomeningeal Metastasis on Cytomorphologic Features of Cerebrospinal Fluid
title_full_unstemmed Deep Learning-Based Classification of Cancer Cell in Leptomeningeal Metastasis on Cytomorphologic Features of Cerebrospinal Fluid
title_short Deep Learning-Based Classification of Cancer Cell in Leptomeningeal Metastasis on Cytomorphologic Features of Cerebrospinal Fluid
title_sort deep learning-based classification of cancer cell in leptomeningeal metastasis on cytomorphologic features of cerebrospinal fluid
topic Oncology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8904144/
https://www.ncbi.nlm.nih.gov/pubmed/35273914
http://dx.doi.org/10.3389/fonc.2022.821594
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